Information Processing Apparatus, Information Processing Method, and Non-Transitory Computer-Readable Medium

This application is based upon and claims the benefit of priority from Japanese patent application No. 2024-208602, filed on Nov. 29, 2024, the disclosure of which is incorporated herein in its entirety by reference.

The present disclosure relates to an information processing apparatus, an information processing method, and a non-transitory computer-readable medium.

A speech recognition technology for automatically generating text from audio data of recorded human speech is known. An example of such a technology is a speech recognition technology described in, for example, “UCORRECT: An Unsupervised Framework for Automatic Speech Recognition Error Correction, ICASSP, 2023”.

“UCORRECT: An Unsupervised Framework for Automatic Speech Recognition Error Correction, ICASSP, 2023” discloses a speech recognition correction technology for detecting recognition errors in the speech recognition text obtained by converting audio data into a text, generating correction candidates for the recognition errors, and selecting a correction candidate determined to be most appropriate from the correction candidates. However, in the technology of “UCORRECT: An Unsupervised Framework for Automatic Speech Recognition Error Correction, ICASSP, 2023”, since the correction candidates are generated in accordance with the context, there is a case where the correction candidates for the recognition error cannot be appropriately generated in a case where the audio data is highly technical in content. That is, in the technical field, there is a possibility that the correction accuracy is not improved much.

The present disclosure has been made in view of the above problem, and one example object of the present disclosure is to provide a technology for accurately correcting recognition errors in the speech recognition text.

According to an example aspect of the present disclosure, there is provided an information processing apparatus including at least one memory storing instructions, and at least one processor configured to execute the instructions to acquire speech recognition text obtained by converting speech into a text, input the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquire the erroneous words output from the first large language model, acquire one or more phoneme sequences of reading of each of the erroneous words, and output word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold, and input the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, apply the word correction candidate output from the second large language model to the speech recognition text, and output a result.

According to another example aspect of the present disclosure, there is provided an information processing method wherein a computer acquires speech recognition text obtained by converting speech into a text, inputs the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquires the erroneous words output from the first large language model, acquires one or more phoneme sequences of reading of each of the erroneous words, and outputs word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold, and inputs the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applies the word correction candidate output from the second large language model to the speech recognition text, and outputs a result.

According to still another example aspect of the present disclosure, there is provided a non-transitory computer-readable medium storing an information processing program causing a computer to execute processing, the processing including processing of acquiring speech recognition text obtained by converting speech into a text, processing of inputting the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquiring the erroneous words output from the first large language model, processing of acquiring one or more phoneme sequences of reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold, and processing of inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting a result.

According to the example aspects of the present disclosure, there is an exemplary effect that a technology for accurately correcting the recognition error for the speech recognition text can be provided.

Hereinafter, example embodiments of the present disclosure will be described. However, the present disclosure is not limited to the following exemplary example embodiments, and various modifications can be made within a scope described in the claims. For example, example embodiments obtained by appropriately combining technologies (some or all of things or methods) adopted in the following exemplary example embodiments can also be included in the scope of the present disclosure. Example embodiments obtained by appropriately omitting some of the technologies adopted in the following exemplary example embodiments can also be included in the scope of the present disclosure. Effects mentioned in the following exemplary example embodiments are examples of effects expected in the exemplary example embodiments, and do not define extension of the present disclosure. In other words, example embodiments that do not provide the effects mentioned in each of the following exemplary example embodiments can also be included in the scope of the present disclosure.

A first exemplary example embodiment that is an example of the example embodiments of the present disclosure will be described in detail with reference to the drawings. The present exemplary example embodiment is a basic form of each exemplary example embodiment to be described below. An application range of each technology adopted in the present exemplary example embodiment is not limited to the present exemplary example embodiment. That is, each technology adopted in the present exemplary example embodiment can also be adopted in another exemplary example embodiment included in the present disclosure within a range in which no particular technical problem occurs. Each technology illustrated in the drawings referred to for describing the present exemplary example embodiment can also be adopted in other exemplary example embodiments included in the present disclosure within a range in which no particular technical problem occurs.

1 1 1 1 11 12 13 14 1 1 FIG. 1 FIG. 1 FIG. A configuration of an information processing apparatuswill be described with reference to.is a block diagram illustrating a configuration of the information processing apparatus. The information processing apparatusis an apparatus that detects recognition errors of speech recognition text obtained by converting audio data into a text and outputs a correct speech recognition text. As illustrated in, the information processing apparatusincludes an acquisition unit(acquisition means in the claims), an error detection unit(error detection means in the claims), a phoneme distance calculation unit(phoneme distance calculation means in the claims), and a sentence correction unit(sentence correction means in the claims). Hereinafter, each unit of the information processing apparatuswill be described.

11 11 1 11 1 The acquisition unitacquires a speech recognition text obtained by converting speech into a text. The speech recognition text can be generated from data recorded with speech using a known technology. The speech recognition text (hereinafter, also simply referred to as “text”) may be recorded in any memory or database, and the acquisition unitmay acquire the speech recognition text recorded in advance and record the speech recognition text in the memory of the information processing apparatus. Alternatively, the acquisition unitmay generate speech recognition text from the audio data using a program for generating the speech recognition text from the audio data, record the speech recognition text in the memory of the information processing apparatus, and acquire the speech recognition text.

12 12 12 The error detection unitinputs the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquires the erroneous words output from the first large language model. The large language model (LLM) is any existing neural network model trained using a large amount of language data. For this large language model, the error detection unitinputs a prompt such as “Please extract erroneous words from the next sentence” along with the speech recognition text, such that the erroneous words (words considered to be incorrect) are output from the large language model based on portions where the context is inconsistent, and the like. The error detection unitacquires the output erroneous words.

13 13 13 13 The phoneme distance calculation unitacquires one or more phoneme sequences for the reading of each of the erroneous words, and outputs word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold. In a case where the speech recognition text is Japanese, the erroneous words may be not only the words (Kanji characters, hiragana, katakana, and the like) but also a katakana sequence or a character string that is not a word. In a case where the speech recognition text is English, the erroneous word is a single alphabetical word. The phoneme distance calculation unitacquires one or more phoneme sequences for the reading of such an erroneous word. In a case where there are a plurality of reading ways for Kanji characters, the phoneme distance calculation unitacquires a plurality of reading ways. The phoneme distance calculation unitoutputs word correction candidates in which a normalized phoneme distance between two phonemes of the acquired phoneme sequence is equal to or less than a predetermined threshold. A phoneme is the smallest unit of sound that corresponds to a consonant or a vowel. Therefore, it is not the same as a syllable. For example, the vowel phonemes are a, i, u, e, and o, and the consonant phonemes are k (K-row), s (S-row), and t (T-row). It also includes nasal sounds and geminate consonants. Punctuation marks may be regarded as silent phonemes. The phoneme distance is an index that represents the ease of recognizing the difference between two phonemes. For example, the larger the phoneme distance, the greater the difference, and it is thus determined to be a phoneme that is less likely to be mistaken. Therefore, a word correction candidate including a phoneme sequence in which the sum of the normalized phoneme distances is equal to or less than a predetermined threshold is selected and output. The “normalization” refers to, for example, dividing the total value of the phoneme distances by the number of phonemes. Since a word is composed of a plurality of phonemes, the sum of phoneme distances also increases as the length of the word increases. Therefore, by dividing the total value of the phoneme distances by the number of phonemes, the phoneme distances that can be compared between words can be obtained.

14 14 14 14 The sentence correction unitinputs an erroneous word, word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applies the word correction candidate output from the second large language model to the speech recognition text, and outputs the speech recognition text. The sentence correction unitgenerates a prompt such as “Please select a word for correcting the erroneous words in the text from among the word correction candidates” together with the speech recognition text, the erroneous words, and the word correction candidates that is output for the erroneous words, and inputs the prompt to the second large language model. The sentence correction unitacquires the selected word output from the second large language model. The sentence correction unitgenerates and outputs the corrected speech recognition text in place of the selected word. The first large language model and the second large language model may be the same large language model.

14 Alternatively, for example, the sentence correction unitmay generate a prompt such as “Please replace the erroneous words in the text with the most appropriate word correction candidates to create a correct text” in place of the above-described prompt, input the prompt to the second large language model, acquire the entire text of the output “correct text”, and output the entire text as the corrected text as it is.

1 1 As described above, the information processing apparatusincludes the acquisition unit for acquiring the speech recognition text obtained by converting the speech into a text, the error detection unit for inputting the speech recognition text and the prompt for detecting speech recognition erroneous words in the speech recognition text to the first large language model, and acquiring the erroneous words output from the first large language model, the phoneme distance calculation unit for acquiring one or more phoneme sequences of the reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold, and the sentence correction unit for inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to the second large language model, applying a result output from the second large language model to the speech recognition text, and outputting the result. Therefore, in the information processing apparatus, it is possible to obtain an effect that the recognition error of the speech recognition text can be corrected with higher accuracy than in the related art by analyzing the phonemes of the recognition erroneous words.

1 1 1 11 12 13 14 2 FIG. 2 FIG. 2 FIG. A flow of an information processing method Swill be described with reference to.is a flowchart illustrating the flow of the information processing method S. As illustrated in, the information processing method Sincludes text acquisition processing S, erroneous word acquisition processing S, word correction candidate output processing S, and corrected text output processing S.

11 11 11 11 11 The text acquisition processing Sis processing of acquiring speech recognition text obtained by converting speech into a text. The text acquisition processing Sis executed by the acquisition unit(one processor). The content of the text acquisition processing Sis as described for the acquisition unit.

12 12 12 12 12 The erroneous word acquisition processing Sis processing of inputting speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquires the erroneous words output from the first large language model. The erroneous word acquisition processing Sis executed by the error detection unit(one processor). The content of the erroneous word acquisition processing Sis as described for the error detection unit.

13 13 13 13 13 The word correction candidate output processing Sis processing of acquiring one or more phoneme sequences for the reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold. The word correction candidate output processing Sis executed by the phoneme distance calculation unit(one processor). The content of the word correction candidate output processing Sis as described for the phoneme distance calculation unit.

14 14 14 14 14 The corrected text output processing Sis processing of inputting an erroneous word, word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the error word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting the result. The corrected text output processing Sis executed by the sentence correction unit(one processor). The content of the corrected text output processing Sis as described for the sentence correction unit.

1 1 As described above, the information processing method Sincludes causing at least one processor execute the text acquisition processing of acquiring the speech recognition text obtained by converting the speech into a text, the erroneous word acquisition processing of inputting the speech recognition text and the prompt for detecting speech recognition erroneous words in the speech recognition text to the first large language model, and acquiring the erroneous words output from the first large language model, the word correction candidate output processing of acquiring one or more phoneme sequences of the reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold, and the corrected text output processing of inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to the second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting the speech recognition text. Therefore, in the information processing method S, it is possible to obtain an effect that the recognition error of the speech recognition text can be corrected with higher accuracy than in the related art by analyzing the phonemes of the recognition erroneous words.

A second exemplary example embodiment that is an example of the example embodiments of the present disclosure will be described in detail with reference to the drawings. Components that have the same functions as the components described in the above-described exemplary example embodiment are denoted by the same reference signs, and will not be described as appropriate. An application range of each technology adopted in the present exemplary example embodiment is not limited to the present exemplary example embodiment. That is, each technology adopted in the present exemplary example embodiment can also be adopted in another exemplary example embodiment included in the present disclosure within a range in which no particular technical problem occurs. Each technology illustrated in each of the drawings referred to for describing the present exemplary example embodiment can also be adopted in another exemplary example embodiment included in the present disclosure within a range in which no particular technical problem occurs.

1 1 1 20 30 40 11 12 13 14 1 13 131 132 1 70 1 1 3 FIG. 3 FIG. A configuration of an information processing apparatusA will be described with reference to.is a block diagram illustrating the configuration of the information processing apparatusA. The information processing apparatusA includes an input/output interface (input/output IF), at least one processor, and at least one memoryin addition to the acquisition unit, the error detection unit, the phoneme distance calculation unit, and the sentence correction unitincluded in the information processing apparatus. The phoneme distance calculation unitincludes a word reading dictionaryand a phoneme distance table. The information processing apparatusA may be connected to a display unit (display). Hereinafter, functions other than the functions of the information processing apparatusdescribed in the first exemplary example embodiment will be described for units of the information processing apparatusA.

30 30 The processorcan be configured using a general-purpose processor such as at least one micro processing unit (MPU) or a central processing unit (CPU). The processormay include a dedicated processor including an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and a programmable logic device (PLD).

40 40 20 11 12 13 14 40 The memorymay include a plurality of types of memories such as a read only memory (ROM) and a random access memory (RAM). The memorymay include a built-in or external memory such as a hard disk drive (HDD) or a solid state drive (SSD). As an example, the processorimplements functions as the acquisition unit, the error detection unit, the phoneme distance calculation unit, and the sentence correction unitby loading various control programs recorded in the ROM of the memoryinto the RAM and executing the programs. Various programs and data such as speech recognition text may be recorded in a cloud database (not illustrated) or the like disposed outside.

20 20 100 20 50 60 100 The input/output IFis an interface that transmits and receives data to and from the outside. Communication between the input/output IFand the outside may be performed, for example, via the Internet. The input/output IFmay include, for example, a short-range communication apparatus such as WiFi (registered trademark) or Bluetooth (registered trademark), which can wirelessly connect to an Internet access point. A wired connection interface such as a USB connector may be used. For example, communication with a first large language modeland a second large language modelis performed via the Internet.

12 13 13 The error detection unitmay acquire information regarding the topic of the speech recognition text together with the erroneous word. The information regarding the topic may be a concept representing the topic or may be a word frequently appearing in the topic. The phoneme distance calculation unitcan narrow down the word correction candidates using the information regarding the topic. For example, in a case where a large number of word correction candidates are listed, for example, the phoneme distance calculation unitmay evaluate the degree of relevance of the word correction candidates to the topic and extract a word correction candidate with the highest relevance.

13 131 131 131 4 FIG. 4 FIG. The phoneme distance calculation unitacquires the phoneme sequence of the erroneous word using the word reading dictionary. An example of the word reading dictionary is illustrated in. The word reading dictionaryillustrated inis a dictionary in which words (kanji) and their reading (or phoneme sequence) are associated with each other. In the word reading dictionary, for example, it is recorded that the reading of a word “motivation” (hiragana phoneme sequence) is “douki” in Japanese, and the phoneme sequence indicating the reading in the alphabet is “douki”. The same reading (phoneme sequence) is recorded for the words “palpitation” and “synchronization”. Only the word and one of the phoneme sequences may be recorded in the word reading dictionary.

13 132 5 FIG. The phoneme distance calculation unitmay derive the phoneme distance using the phoneme distance table in which the distance between two phonemes is defined. An example of the phoneme distance table is illustrated in. The phoneme distance tableshows a table in which the inter-phoneme distance for the phonemes of the “A-row” (a, i, u, e, and o) is recorded. For example, since the phonemes of “a” and “a” are the same, the phoneme distance is zero. The phoneme distance between “a” and “i” is 0.9. The phoneme distance is a numerical value between zero and one, and the closer the phoneme distance (the more similar the pronunciation), the smaller the numerical value. Note that there is also a table in which phoneme distances between phonemes in the “A” row and phonemes in the other rows are recorded, and there is also a similar table for phonemes other than the A-row.

13 132 132 In a case of outputting the word correction candidates, the phoneme distance calculation unitselects and outputs a word correction candidate for which a numerical value indicating the smallest possible difference from the erroneous word is obtained. That is, the phoneme distance tableis a cost table, and a word including a combination of phonemes for which the cost calculated using the cost table is as low as possible is selected as the word correction candidate. The phoneme distance tableis created in advance.

132 The phoneme distance tablemay be, for example, an inter-phoneme cost table created by a model trained using pair data of an erroneous word included in the recognition text of the speech acquired under a common condition and the corresponding correct word. The common condition refers to a condition in which a topic (domain), a recording environment (place, room, microphone, and the like) of audio data, a speech recognition model, and the like are similar or the same. The machine learning model is trained by using a large number of pieces of training data including the erroneous word and the correct word included in the speech recognition text of the audio data acquired under such a condition. Using the machine learning model trained in this way, the phoneme distance (cost) between any two phonemes can be evaluated and tabulated.

7 FIG. 1 1 1 132 13 is a schematic diagram illustrating a method for generating a phoneme distance table using a trained machine learning model. First, a dataset including an erroneous wordA and a correct wordB is set as pair D. Training data D including n such pairs is input to the untrained machine learning model M for training. Such training is iterated to generate the trained machine learning model LM. The machine learning model M can be trained on the confusability (cost) between phonemes from a combination of phoneme sequences of an erroneous word and a correct word. The phoneme distance table output from the trained machine learning model LM can be used as the phoneme distance tableused by the phoneme distance calculation unit.

13 In a case where there is no word correction candidate in which the normalized phoneme distance is equal to or less than a predetermined threshold, the phoneme distance calculation unitmay output a word correction candidate having the smallest normalized phoneme distance from among the evaluated word correction candidates. Alternatively, a plurality of word correction candidates including the word correction candidate having the smallest normalized phoneme distance may be output.

13 131 131 13 4 FIG. The word reading dictionary may include technical term flags. Each of the technical term flags may be added by a user (expert), or may be added by using, for example, a technical term list of a field for targets collected in advance by the phoneme distance calculation unitusing an LLM or the like, or a publicly available technical term dictionary. In the word reading dictionaryillustrated in, flags TA, which indicate technical terms, are respectively added to the word “palpitation” and the word “tumor”. The word reading dictionaryis a dictionary for correcting errors of the speech recognition text in the medical field. Therefore, the flags TA are respectively added to the word “palpitation” and the word “tumor” as technical terms in the medical field. The phoneme distance calculation unitmay add a weight to the word to which the flag TA is added in obtaining the phoneme distance. Adding a weight indicates processing of increasing the evaluation value, and corresponds to performing processing of reducing the cost in the present exemplary example embodiment.

14 12 The sentence correction unitmay select a word correction candidate based on the information acquired by the error detection unitusing the second large language model generated by Retrieval-Augmented Generation. The Retrieval-Augmented Generation (RAG) is a method for accurately correcting errors in the speech recognition text related to a technical field by, for example, inputting technical term data to a large language model for retraining the large language model.

12 12 12 14 14 Specifically, the error detection unitperforms error detection using, for example, a general-purpose large language model tuned for medical use. The text region is narrowed down based on the remaining words that are not determined to be erroneous. For example, in a case where the error detection unitcan narrow down the content of the text to a clinical department, the error detection unittransmits the information to the sentence correction unit. The sentence correction unitselects a word correction candidate using the second large language model generated by Retrieval-Augmented Generation, which is restricted to the field of a “clinical department”. By such a method, it is possible to accurately correct errors in the speech recognition text.

6 FIG. 2 1 11 21 is a flowchart illustrating an example of an information processing method Sexecuted by the information processing apparatusA. First, the acquisition unitacquires speech recognition text TX (step S). It is assumed that there is a sentence “Motivation and dizziness occur due to anemia or hypotension” in the word correction candidate.

12 22 13 13 131 13 23 14 24 4 FIG. On the other hand, the error detection unitacquires the phrase “motivation” (which reads “douki” in Japanese) as an erroneous word (step S). Next, the phoneme distance calculation unitacquires a phoneme sequence of the reading of “motivation”. For example, the phoneme distance calculation unitacquires “douki” using the word reading dictionaryillustrated in. Next, the phoneme distance calculation unitoutputs word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence “douki” is equal to or less than a predetermined threshold. For example, three words of “motivation”, “palpitation”, and “synchronization” (which all the words read “douki” in Japanese) in which the sum of the phoneme distances is zero in the same reading are output (step S). Next, the sentence correction unitinputs these words together with the speech recognition text to the second large language model. “Palpitation and dizziness occur due to anemia or hypotension”, which is the correct text output from the second large language model, is acquired, the sentence of the original speech recognition text is replaced with the correct text, and the result is output (step S).

1 13 1 1 As described above, in the information processing apparatusA, a configuration in which the phoneme distance calculation unitacquires the phoneme sequence of the erroneous word using the word reading dictionary is adopted. Therefore, in the information processing apparatusA, in addition to the effects obtained by the information processing apparatus, it is possible to obtain an effect that the correct phoneme sequence of the erroneous word can be efficiently acquired.

1 13 1 1 In the information processing apparatusA, a configuration is adopted in which the phoneme distance calculation unitderives the phoneme distance using the phoneme distance table in which the distance between two phonemes is defined. Therefore, in the information processing apparatusA, in addition to the effects obtained by the information processing apparatus, it is possible to obtain an effect that the phoneme distance can be derived efficiently.

1 12 13 1 1 In the information processing apparatusA, a configuration is adopted in which the error detection unitacquires information regarding the topic of the speech recognition text together with the erroneous word, and the phoneme distance calculation unitnarrows down the word correction candidate using the information. Therefore, in the information processing apparatusA, in addition to the effects obtained by the information processing apparatus, it is possible to obtain an effect that the word correction candidates can be accurately narrowed down.

1 14 12 1 1 In the information processing apparatusA, a configuration is adopted in which the sentence correction unitselects a word correction candidate based on the information acquired by the error detection unitusing the second large language model generated by Retrieval-Augmented Generation. Therefore, in the information processing apparatusA, in addition to the effects obtained by the information processing apparatus, it is possible to obtain an effect that the word correction candidates can be more accurately selected.

1 13 1 1 In the information processing apparatusA, a configuration is adopted in which in a case where there is no word correction candidate in which the normalized phoneme distance is equal to or less than a predetermined threshold, the phoneme distance calculation unitoutputs a word correction candidate having the smallest normalized phoneme distance. Therefore, in the information processing apparatusA, in addition to the effects obtained by the information processing apparatus, it is possible to obtain an effect that the word correction candidate considered to be the most appropriate can be output even in a case where no word correction candidate satisfying a predetermined condition is found.

1 131 13 1 1 In the information processing apparatusA, a configuration is adopted in which technical term flags are added to the word reading dictionary, and the phoneme distance calculation unitadds a weight to the word to which a flag is added in obtaining the phoneme distance. Therefore, in the information processing apparatusA, in addition to the effects obtained by the information processing apparatus, it is possible to obtain an effect that speech recognition text correction can be performed more accurately for a predetermined technical field.

1 132 1 1 In the information processing apparatusA, a configuration is adopted in which the phoneme distance tableis an inter-phoneme cost table created by a model trained using pair data of an erroneous word included in the recognition text of the speech acquired under a common condition and the corresponding correct word. Therefore, in the information processing apparatusA, in addition to the effects obtained by the information processing apparatus, it is possible to obtain an effect that speech recognition text correction can be performed more accurately on the speech recognition text acquired under specific conditions.

1 1 Some or all of the functions of the information processing apparatusesandA (hereinafter, also referred to as “each of the above-described apparatuses”) may be implemented by hardware such as an integrated circuit (IC chip) or may be implemented by software.

8 FIG. 8 FIG. In the latter case, each of the above-described apparatuses is implemented by, for example, a computer that executes a command of a program that is software for implementing each function. An example of such a computer (hereinafter, referred to as a computer C) is illustrated in.is a block diagram illustrating a hardware configuration of the computer C functioning as each of the above-described apparatuses.

1 2 2 1 2 The computer C includes at least one processor Cand at least one memory C. A program P for causing the computer C to operate as each of the above-described apparatuses is recorded in the memory C. In the computer C, the processor Creads the program P from the memory Cand executes the program P to implement each function of each of the above-described apparatuses.

1 2 As the processor C, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, or a combination of these can be used. As the memory C, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination of these can be used.

The computer C may further include a random access memory (RAM) for loading the program P at the time of execution and temporarily storing various types of data. The computer C may further include a communication interface for transmitting and receiving data to and from another apparatus. The computer C may further include an input/output interface for connecting input/output apparatuses such as a keyboard, a mouse, a display, and a printer.

The program P can be recorded in a non-transitory tangible recording medium M readable by the computer C. As such a recording medium M, for example, a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used. The computer C can acquire the program P via such a recording medium M. The program P can be transmitted via a transmission medium. As such a transmission medium, for example, a communication network, a broadcast wave, or the like can be used. The computer C can also acquire the program P via such a transmission medium.

The program P can be stored and provided to a computer using any type of non-transitory computer readable media M. Non-transitory computer readable media M include any type of tangible storage media. Examples of non-transitory computer readable media include magnetic storage media (such as floppy disks, magnetic tapes, hard disk drives, etc.), optical magnetic storage media (e.g. magneto-optical disks), CD-ROM (compact disc read only memory), CD-R (compact disc recordable), CD-R/W (compact disc rewritable), and semiconductor memories (such as mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash ROM, RAM, etc.). The program P may be provided to the computer C using any type of transitory computer readable media. Examples of transitory computer readable media include electric signals, optical signals, and electromagnetic waves. Transitory computer readable media can provide the program P to the computer C via a wired communication line (e.g. electric wires, and optical fibers) or a wireless communication line.

Each of the above-described functions of each of the above-described apparatuses may be implemented by a single processor provided in a single computer, may be implemented in cooperation with a plurality of processors provided in a single computer, or may be implemented in cooperation with a plurality of processors provided in a plurality of computers. The program for causing each of the above-described apparatuses to implement each of the above-described functions may be stored in a single memory provided in a single computer, may be stored in a distributed manner in a plurality of memories provided in a single computer, or may be stored in a distributed manner in a plurality of memories provided in a plurality of computers.

The present disclosure includes the technologies described in the following Supplementary Notes. However, the present disclosure is not limited to the technologies described in each of Supplementary Notes below, and various modifications can be made within the scope described in the claims.

An information processing apparatus including: an acquisition means for acquiring speech recognition text obtained by converting speech into a text; an error detection means for inputting the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquiring the erroneous words output from the first large language model; a phoneme distance calculation means for acquiring one or more phoneme sequences of reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold; and a sentence correction means for inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting a result.

The information processing apparatus according to Supplementary Note 1, in which the phoneme distance calculation means acquires the phoneme sequence of the erroneous word using a word reading dictionary.

The information processing apparatus according to Supplementary Note 1 or 2, in which the phoneme distance calculation means derives the phoneme distance using a phoneme distance table in which a distance between two phonemes is defined.

The information processing apparatus according to any one of Supplementary Notes 1 to 3, in which the error detection means acquires information regarding a topic of the speech recognition text together with the erroneous word, and the phoneme distance calculation means narrows down the word correction candidate using the information.

The information processing apparatus according to Supplementary Note 4, in which the sentence correction means selects the word correction candidate based on the information acquired by the error detection means, using the second large language model generated by Retrieval-Augmented Generation.

5 The information processing apparatus according to any one of Supplementary Notes 1 to, in which in a case where there is no word correction candidate in which the normalized phoneme distance is equal to or less than a predetermined threshold, the phoneme distance calculation means outputs the word correction candidate having a smallest normalized phoneme distance.

The information processing apparatus according to Supplementary Note 2, in which a technical term flag is added to the word reading dictionary, and the phoneme distance calculation means adds a weight to a word to which the flag is added in obtaining the phoneme distance.

The information processing apparatus according to Supplementary Note 3, in which the phoneme distance table is an inter-phoneme cost table created by a model trained using pair data of an erroneous word included in recognition text of speech acquired under a common condition and a corresponding correct word.

An information processing method including: acquiring speech recognition text obtained by converting speech into a text; inputting the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquiring the erroneous words output from the first large language model; acquiring one or more phoneme sequences of reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold; and inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting a result.

An information processing program causing a computer to execute processing, the processing including: processing of acquiring speech recognition text obtained by converting speech into a text; processing of inputting the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquiring the erroneous words output from the first large language model; processing of acquiring one or more phoneme sequences of reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold; and processing of inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting a result.

The present disclosure includes the technologies described in the following Supplementary Notes. However, the present disclosure is not limited to the technologies described in each of Supplementary Notes below, and various modifications can be made within the scope described in the claims.

An information processing apparatus including at least one processor, in which the at least one processor executes: acquisition processing of acquiring speech recognition text obtained by converting speech into a text; error detection processing of inputting the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquiring the erroneous words output from the first large language model; phoneme distance calculation processing of acquiring one or more phoneme sequences of reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold; and sentence correction processing of inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting a result.

The information processing apparatus may further include a memory. The memory may store a program for causing the at least one processor to execute each type of the processing.

The information processing apparatus according to Supplementary Note 21, in which in the phoneme distance calculation processing, the at least one processor acquires the phoneme sequence of the erroneous word using a word reading dictionary.

The information processing apparatus according to Supplementary Note 21, in which in the phoneme distance calculation processing, the at least one processor derives the phoneme distance using a phoneme distance table in which a distance between two phonemes is defined.

The information processing apparatus according to Supplementary Note 21, in which in the error detection processing, the at least one processor acquires information regarding a topic of the speech recognition text together with the erroneous word, and the phoneme distance calculation processing narrows down the word correction candidate using the information.

The information processing apparatus according to Supplementary Note 24, in which in the sentence correction processing, the at least one processor selects the word correction candidate based on the information acquired in the error detection processing using the second large language model generated by Retrieval-Augmented Generation.

The information processing apparatus according to Supplementary Note 21, in which in the phoneme distance calculation processing, in a case where there is no word correction candidate in which the normalized phoneme distance is equal to or less than a predetermined threshold, the at least one processor outputs the word correction candidate having a smallest normalized phoneme distance.

The information processing apparatus according to Supplementary Note 22, in which a technical term flag is added to the word reading dictionary, and the phoneme distance calculation processing adds a weight to a word to which the flag is added in obtaining the phoneme distance.

The information processing apparatus according to Supplementary Note 23, in which the phoneme distance table is an inter-phoneme cost table created by a model trained using pair data of an erroneous word included in recognition text of speech acquired under a common condition and a corresponding correct word.

An information processing method causing at least one processor to execute: acquisition processing of acquiring speech recognition text obtained by converting speech into a text; error detection processing of inputting the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquiring the erroneous words output from the first large language model; phoneme distance calculation processing of acquiring one or more phoneme sequences of reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold; and sentence correction processing of inputting the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting a result.

The present disclosure includes the technologies described in the following Supplementary Notes. However, the present disclosure is not limited to the technologies described in each of Supplementary Notes below, and various modifications can be made within the scope described in the claims.

An information processing method including: acquisition processing of acquiring, by at least one processor, speech recognition text obtained by converting speech into a text; error detection processing of inputting, by the at least one processor, the speech recognition text and a prompt for detecting speech recognition erroneous words in the speech recognition text to a first large language model, and acquiring the erroneous words output from the first large language model; phoneme distance calculation processing of acquiring, by the at least one processor, one or more phoneme sequences of reading of each of the erroneous words, and outputting word correction candidates in which a normalized phoneme distance between two phonemes of the phoneme sequence is equal to or less than a predetermined threshold; and sentence correction processing of inputting, by the at least one processor, the erroneous word, the word correction candidates output based on the erroneous word, and a prompt for instructing to select a word correction candidate to replace the erroneous word to a second large language model, applying the word correction candidate output from the second large language model to the speech recognition text, and outputting a result.

The information processing method according to Supplementary Note 31, in which in the phoneme distance calculation processing, the at least one processor acquires the phoneme sequence of the erroneous word using a word reading dictionary.

The information processing method according to Supplementary Note 31 or 32, in which the phoneme distance calculation processing, the at least one processor derives the phoneme distance using a phoneme distance table in which a distance between two phonemes is defined.

33 The information processing method according to any one of Supplementary Notes 31 to, in which in the error detection processing, the at least one processor acquires information regarding a topic of the speech recognition text together with the erroneous word, and in the phoneme distance calculation processing, the word correction candidate is narrowed down using the information.

The information processing method according to Supplementary Note 34, in which in the sentence correction processing, the at least one processor selects the word correction candidate based on the acquired information using the second large language model generated by Retrieval-Augmented Generation.

35 The information processing method according to any one of Supplementary Notes 31 to, in which in the phoneme distance calculation processing, in a case where there is no word correction candidate in which the normalized phoneme distance is equal to or less than a predetermined threshold, the at least one processor outputs the word correction candidate having a smallest normalized phoneme distance.

The information processing method according to Supplementary Note 32, in which a technical term flag is added to the word reading dictionary, and in the phoneme distance calculation processing, a weight is added to a word to which the flag is added in obtaining the phoneme distance.

The information processing method according to Supplementary Note 33, in which the phoneme distance table is an inter-phoneme cost table created by a model trained using pair data of an erroneous word included in recognition text of speech acquired under a common condition and a corresponding correct word.